Chlorinated ethenes such as tetrachloroethene (PCE) and trichloloethene (TCE) are among the most prevalent contaminants in soil, sediments and groundwaters. Currently, Insitu bioremediation via anaerobic reductive dechlorination has become a widely used technology for groundwater contaminated with chlorinated ethenes. To better understand the reductive dechlorination remediation process and the inter-relationships among the complex microbial communities that comprise it, a comprehensive biokinetic model was recently developed at Cornell University by Gretchen Heavner, a modification of an earlier Cornell model developed by Donna Fennell. The Heavner model uses specific biomasses based on quantitative PCR-based population data, and under some conditions can accurately predict kinetics of dechlorination, fermentation of electron donors, and competition for electron donors between dechlorinators and methanogens, and generation of methane. However, the platform used to run the model - STELLA® (High Performance Systems) - is cumbersome for simulation of long time-spans, limiting the model's utility. Furthermore, the model uses an empirical, "mRNA-tuning" technique to improve data fits at high PCE-loadings, which makes the model descriptive, rather than predictive, in such cases. Additionally, electron donor fermentation is not predicted well at high electron-donor feeding rates. The overall purpose of this thesis research was to address some of the limitations of the Heavner model. The STELLA® model was successfully converted to run in MATLAB® using Runge-Kutta 4th-order integration. The model fits at high-PCE and high electron-donor loadings were improved by utilizing the inhibitory effects of high PCE on dechlorination and methanogenesis, and by postulating additional pathways of butyrate's fermentation and acetate's hydrogenation to storage products. Model simulations indicate that by adding 2nd-order Haldane inhibition instead of mRNA tuning, the model revised in this thesis research predicts the dechlorination, methanogenesis and donor fermentation well over a broad range of PCE feeding rates. Moreover, when simulating donor fermentation at high-PCE-loadings, butyrate's fermentations and acetate's hydrogenation to storage products must be considered to obtain a mass balance between butyrate consumption and product formation.